Explaining Controversy on Social Media via Stance Summarization
In an era in which new controversies rapidly emerge and evolve on social media, navigating social media platforms to learn about a new controversy can be an overwhelming task. In this light, there has been significant work that studies how to identify and measure controversy online. However, we currently lack a tool for effectively understanding controversy in social media. For example, users have to manually examine postings to find the arguments of conflicting stances that make up the controversy. In this paper, we study methods to generate a stance-aware summary that explains a given controversy by collecting arguments of two conflicting stances. We focus on Twitter and treat stance summarization as a ranking problem of finding the top k tweets that best summarize the two conflicting stances of a controversial topic. We formalize the characteristics of a good stance summary and propose a ranking model accordingly. We first evaluate our methods on five controversial topics on Twitter. Our user evaluation shows that our methods consistently outperform other baseline techniques in generating a summary that explains the given controversy.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Vocabulary-based Method for Quantifying Controversy in Social Media
Identifying controversial topics is not only interesting from a social point of view, it also enables the application of methods to avoid the information segregation, creating better discussion contexts and reaching agre…
Scientific Opinion Summarization: Paper Meta-review Generation Dataset, Methods, and Evaluation
Opinions in scientific research papers can be divergent, leading to controversies among reviewers. However, most existing datasets for opinion summarization are centered around product reviews and assume that the analyze…
Opinion SummarizationReview GenerationText GenerationExplaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approach
The rapid dissemination of misinformation through social media increased the importance of automated fact-checking. Furthermore, studies on what deep neural models pay attention to when making predictions have increased …
Explanation GenerationFact CheckingMisinformationText SummarizationA Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit
Bioethical debates increasingly unfold on social media, yet stance detection research lacks large-scale, domain-specific resources for modeling such context-dependent discourse. We present BioStance, a context-aware data…
Stance DetectionArgument MiningESG Reputation Risk Matters: An Event Study Based on Social Media Data
We investigate the response of shareholders to Environmental, Social, and Governance-related reputational risk (ESG-risk), focusing exclusively on the impact of social media. Using a dataset of 114 million tweets about f…